1. Technical Field
The present subject matter relates to discrete multi-tone (DMT) communication systems. More particularly, the subject matter relates to the mapping of data to subcarrier frequencies or “tones” within a DMT signal.
2. Background Information
Discrete multi-tone (DMT) modulation has become a pervasive method for transmitting digital data. This data transmission scheme uses a series of subcarrier frequency bins or “tones,” each with a unique center frequency. These tones are spread out within a frequency range or “band,” each tone potentially carrying one or more bits of a larger unit of digital data. The tones are all modulated together in the frequency-domain so as to produce a waveform in the time-domain that is transmitted across a media (e.g., a twisted pair telephone cable) and received and demodulated at a receiver.
DMT modulation has found widespread use because it allows for simple equalizer design, allows for flexibility in the transmit spectrum, and is able to approach channel capacity through appropriate bit loading and gain scaling. Several different error detection and correction coding schemes have evolved, and many are used, both separately and in concert, within DMT transmissions. One such class of coding schemes, known as convolutional codes, operates on one or more bits at a time within a serial stream of data bits as they are received.
Many convolutional decoding algorithms (e.g., the Viterbi algorithm) tend to perform less efficiently when noise is present if the noise correlates to multiple, adjacent bits within a data stream. This means that if the noise affects multiple adjacent tones within a DMT signal, and if the noisy tones represent bits that are input in sequence into a decoder at the receiver, the decoder will be more likely to make errors.
To counter this effect, some data transmission standards (e.g., ADSL2) allow for random assignments of bits or groups of bits to the various DMT tones. This is known as “tone ordering.” By appropriately assigning bits or groups of bits to the different tones within a band, noise bursts that affect adjacent groups of tones will affect groups of bits that are not all adjacent to each other in the demodulated bit stream, allowing convolutional decoders used at the receiver to operate more efficiently.
Tone ordering requires both the transmitter and receiver of a DMT signal to maintain a mapping of the bit(s)-to-tone, and tone-to-bit(s), as well as tables detailing other information (e.g., the number of bits per tone) for each tone. Such tables must be stored in both the transmitter and the receiver, thus utilizing some resources of each (e.g., space within a memory device). For DMT systems such as ADSL or ADSL2, this was not a significant problem given the number of tones that these technologies use (generally not more than a few hundred tones). Newer DMT technologies such as VDSL, however, may use as many as 4,096 tones, and the amount of memory needed for the tables, as well as the time required to traverse them during processing, is thus proportionally larger.
Proposals have been presented in the context of VDSL2 to reduce the table sizes by grouping consecutive tones together and storing information organized or indexed by group (see M. Peters, Tone Grouping for VDSL2, ITU-T Study Group 15 Question 4, LB-051, June 2004). But grouping consecutive tones together keeps adjacent bits within the data stream together, and the above-described effects related to correlated noise and convolutional decoders resurface. Further, as the group size is increased to produce greater resource efficiency, the decoder sensitivity to correlated noise also increases, resulting in a decrease in the performance of the decoder. A method and system that allows grouping of tones for increased resource efficiency, but without a corresponding decrease in decoder performance is thus desirable.